يعرض 1 - 20 نتائج من 231 نتيجة بحث عن '"Taylor, Jeremy M.G."', وقت الاستعلام: 0.75s تنقيح النتائج
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    المصدر: International Statistical Review / Revue Internationale de Statistique, 2019 Aug 01. 87(2), 393-418.

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    المصدر: Kragh Andersen , P , Pohar Perme , M , van Houwelingen , H C , Cook , R J , Joly , P , Martinussen , T , Taylor , J M G , Abrahamowicz , M & Therneau , T M 2021 , ' Analysis of time-to-event for observational studies : Guidance to the use of intensity models ' , Statistics in Medicine , vol. 40 , no. 1 , pp. 185-211 . https://doi.org/10.1002/sim.8757

    وصف الملف: application/pdf

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    وصف الملف: application/pdf

    Relation: Suresh, Krithika; Taylor, Jeremy M.G.; Tsodikov, Alexander (2021). "A copula‐based approach for dynamic prediction of survival with a binary time‐dependent covariate." Statistics in Medicine 40(23): 4931-4946.; https://hdl.handle.net/2027.42/170255; Statistics in Medicine; Van Houwelingen HC, Putter H. Dynamic predicting by landmarking as an alternative for multi‐state modeling: an application to acute lymphoid leukemia data. Lifetime Data Anal. 2008; 14 ( 4 ): 447.; Ferrer L, Putter H, Proust‐Lima C. Individual dynamic predictions using landmarking and joint modelling: validation of estimators and robustness assessment. Stat Methods Med Res. 2019; 28 ( 12 ): 3649 ‐ 3666.; Suresh K, Taylor JMG, Tsodikov A. A Gaussian copula approach for dynamic prediction of survival with a longitudinal biomarker. Biostatistics. 2019. https://doi.org/10.1093/biostatistics/kxz049.; Andersen PK, Borgan O, Gill RD, Keiding N. Statistical Models Based on Counting Processes. Berlin, Germany: Springer Science & Business Media; 2012.; Hougaard P. Multi‐state models: a review. Lifetime Data Anal. 1999; 5 ( 3 ): 239 ‐ 264.; Aalen OO, Johansen S. An empirical transition matrix for non‐homogeneous Markov chains based on censored observations. Scand J Stat. 1978; 5 ( 3 ): 141 ‐ 150.; Fiocco M, Putter H, Houwelingen HC. Reduced‐rank proportional hazards regression and simulation‐based prediction for multi‐state models. Stat Med. 2008; 27 ( 21 ): 4340 ‐ 4358.; Suresh K, Taylor JMG, Spratt DE, Daignault S, Tsodikov A. Comparison of joint modeling and landmarking for dynamic prediction under an illness‐death model. Biom J. 2017; 59 ( 6 ): 1277 ‐ 1300.; Jewell Nicholas P, Nielsen JP. A framework for consistent prediction rules based on markers. Biometrika. 1993; 80 ( 1 ): 153 ‐ 164.; Blanche P, Proust‐Lima C, Loubère L, Berr C, Dartigues J‐F, Jacqmin‐Gadda H. Quantifying and comparing dynamic predictive accuracy of joint models for longitudinal marker and time‐to‐event in presence of censoring and competing risks. Biometrics. 2015; 71 ( 1 ): 102 ‐ 113.; Efron B, Tibshirani RJ. An Introduction to the Bootstrap. Boca Raton, FL: CRC Press; 1994.; Prenen L, Braekers R, Duchateau L. Extending the Archimedean copula methodology to model multivariate survival data grouped in clusters of variable size. J R Stat Soc Ser B (Stat Methodol). 2017; 79 ( 2 ): 483 ‐ 505.; Spiekerman CF, Lin DY. Marginal regression models for multivariate failure time data. J Am Stat Assoc. 1998; 93 ( 443 ): 1164 ‐ 1175.; Song X‐K. Correlated Data Analysis: Modeling, Analytics, and Applications. Berlin, Germany: Springer Science & Business Media; 2007.; Zeger SL, Liang KY. Longitudinal data analysis for discrete and continuous outcomes. Biometrics. 1986; 42 ( 1 ): 121 ‐ 130.; Andersen PK, Gill RD. Cox’s regression model for counting processes: a large sample study. Ann Stat. 1982; 10 ( 4 ): 1100 ‐ 1120.; Joe H, Xu JJ. The Estimation Method of Inference Functions for Margins for Multivariate Models. Technical Report. University of British Columbia: Department of Statistics; 1996.; Sklar A. Fonctions de répartition à n dimensions et leurs marges. Publications de l’Institut de Statistique de l’Université de Paris. 1959; 8: 229 ‐ 231.; Leon AR, Wu B. Copula‐based regression models for a bivariate mixed discrete and continuous outcome. Stat Med. 2011; 30 ( 2 ): 175 ‐ 185.; Song PX‐K, Li M, Yuan Y. Joint regression analysis of correlated data using Gaussian copulas. Biometrics. 2009; 65 ( 1 ): 60 ‐ 68.; Rizopoulos D. Dynamic predictions and prospective accuracy in joint models for longitudinal and time‐to‐event data. Biometrics. 2011; 67 ( 3 ): 819 ‐ 829.; Rizopoulos D, Murawska M, Andrinopoulou ER, Molenberghs G, Takkenberg JJM, Lesaffre E. Dynamic predictions with time‐dependent covariates in survival analysis using joint modeling and landmarking; 2013. arXiv:1306.6479 [stat.AP].; Rizopoulos D, Molenberghs G, Lesaffre EMEH. Dynamic predictions with time‐dependent covariates in survival analysis using joint modeling and landmarking. Biom J. 2017; 59 ( 6 ): 1261 ‐ 1276.; Houwelingen H, Putter H. Dynamic Prediction in Clinical Survival Analysis. Boca Raton, FL: CRC Press; 2011.; Putter H. dynpred: companion package to "Dynamic prediction in clinical survival analysis." R package version 0.1; 2015:2.

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